Paper Contents
Abstract
Breast cancer is a significant health concern worldwide, and early detection plays a crucial role in improving patient outcomes. In recent years, deep learning approaches have shown promise in accurately predicting breast cancer, aiding in early diagnosis and treatment planning. This research paper presents a comprehensive study on breast cancer prediction using deep learning techniques.The study utilizes a large dataset of mammographic images collected from various medical institutions. Preprocessing techniques are employed to enhance the quality of the data, including image normalization and feature extraction. Several deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are explored to develop predictive models for breast cancer.
Copyright
Copyright © 2024 Mamatha Kurra. This is an open access article distributed under the Creative Commons Attribution License.
